This paper proposed two deep learning approaches for saliency retargeting while preserving aesthetic quality in images, addressing the limitations of conventional methods that focus solely on maximizing object saliency. The first approach, “OperatorNet”, estimates the intensity of multiple image editing operators to enhance the main object, utilizing EfficientNet-lite3 and multitask learning. The second approach, “GeneratorNet”, directly transforms images at the pixel level using the U-Net architecture. Both quantitative evaluations and subjective human assessments were conducted on images generated by the proposed methods. Experiments using the MS-COCO dataset confirmed that the proposed approaches achieved superior saliency retargeting while considering aesthetic quality compared to existing methods.